Dynamic energy-efficient scheduling of multi-variety and small batch flexible job-shop: A case study for the aerospace industry

Abstract

In the context of the global implementation of carbon peaking and carbon neutrality, energy-aware production scheduling has attracted world-wide attention. The optimal scheduling problem for a multi-variety and small-batch dynamic flexible job shop scheduling is challenging in aerospace industry. This paper considers an energy-efficient scheduling problem with multi-resource constraints of multi-variety and small-batch dynamic flexible job shop (α-shop for short). First, it formulates a mathematical model of energy efficient α-shop with the objectives of minimum total energy consumption (TEC), makespan and machining cost. Next, a novel multi-objective bi-population differential artificial bee colony (BDABC) algorithm is proposed to solve the model. Then, the design of experiment Taguchi method is performed to calibrate the parameter setting of the proposed algorithm. Finally, comparative experiments of the proposed algorithm with other popular algorithms such as large-scale multi-objective optimization framework (LSMOF), large-scale multi-objective competitive swarm optimizer (LMOCSO), as well as some known algorithms including multi-objective evolutionary algorithm based on decomposition (MOEA/D), multi-objective particle swarm optimization (MOPSO) and multi-objective artificial bee colony (MOABC) are carried out to assess the effectiveness of the proposed algorithm in an aerospace plant of China based on a number of well-recognized metrics. The experimental results demonstrate that our proposed algorithm is effective reported for this real-life scheduling problem.